GhostConv+CA-YOLOv8n:基于现实复杂背景的低层特征的轻量级网络
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Frontiers in plant science
|August 29, 2025
概括
这项研究介绍了GhostConv+CA-YOLOv8n,这是一种轻量级的深度学习模型,用于高效地检测水害虫. 在复杂的现场条件下实现高精度和回忆,解决现有方法的局限性.
科学领域:
- 农业科学
- 计算机视觉
- 深度学习
背景情况:
- 由于复杂的背景和有限的资源, 大米害虫检测的深度学习模型在现实领域的性能下降.
- 现有的方法对封闭或多种规模的害虫具有不充分的特征表示,并且对于边缘设备而言计算成本昂贵.
研究的目的:
- 开发一个轻量级和高效的物体检测框架,用于在具有挑战性的田间环境中准确检测水害虫.
- 改进特征表示,降低计算成本,增强界限框回归和类不平衡处理以检测害虫.
主要方法:
- 推出了GhostConv+CA-YOLOv8n,一个轻量级的框架,集成GhostConv模块进行参数缩小和上下文聚合 (CA),以增强功能表示.
- 采用Shape-IoU来改善边界框回归,考虑到目标形态和滑动损失,以解决训练期间的类失衡.
- 在RicePest15数据集和IP102基准上对该模型进行了全面的性能分析.
主要成果:
- 在Ricepest15上,GhostConv+CA-YOLOv8n获得了89. 959%的精度和82. 258%的回忆,其性能超过了YOLOv8n的基线,参数减少了1. 34%.
- 该模型在IP102基准上显著改善了mAP (94. 527%与基线84. 994%) 和概括能力.
- 在IP102数据集上取得了显著的F1得分 (4.49%),精度 (5.452%) 和回忆 (3.407%).
结论:
- GhostConv+CA-YOLOv8n提供了一个实用的解决方案,用于实时检测水害虫,
- 拟议的框架有效地解决了场地害虫监测中的封闭,尺度变化和计算限制的挑战.
- 这项研究有助于推进自动化害虫检测系统, 这对于可持续和高效的米种植至关重要.
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